Ryota Sato1,2, Kohsuke Kudo3, Niki Udo4, Masaaki Matsushima5, Ichiro Yabe5, Akinori Yamaguchi3, Khin Khin Tha6, Makoto Sasaki7, Masafumi Harada8, Noriyuki Matsukawa9, Tomoki Amemiya10, Yasuo Kawata11, Yoshitaka Bito3,11, Hisaaki Ochi10,3, Toru Shirai10,3. 1. Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan. ryota.sato.bt@fujifilm.com. 2. Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan. ryota.sato.bt@fujifilm.com. 3. Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan. 4. Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan. 5. Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan. 6. Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Hokkaido, Japan. 7. Institute for Biomedical Sciences, Iwate Medical University, Morioka, Iwate, Japan. 8. Department of Radiology, Tokushima University, Tokushima, Japan. 9. Department of Neurology, Nagoya City University, Nagoya, Aichi, Japan. 10. Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan. 11. Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan.
Abstract
OBJECTIVES: Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM. METHODS: Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis. RESULTS: The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10-10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10-6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05). CONCLUSIONS: In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index. KEY POINTS: • We developed a novel diagnostic index for Alzheimer's disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM). • QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time. • In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.
OBJECTIVES: Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM. METHODS: Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis. RESULTS: The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10-10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10-6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05). CONCLUSIONS: In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index. KEY POINTS: • We developed a novel diagnostic index for Alzheimer's disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM). • QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time. • In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.
Authors: Sanneke van Rooden; Maarten J Versluis; Michael K Liem; Julien Milles; Andrea B Maier; Ania M Oleksik; Andrew G Webb; Mark A van Buchem; Jeroen van der Grond Journal: Alzheimers Dement Date: 2013-05-25 Impact factor: 21.566
Authors: J M G van Bergen; X Li; J Hua; S J Schreiner; S C Steininger; F C Quevenco; M Wyss; A F Gietl; V Treyer; S E Leh; F Buck; R M Nitsch; K P Pruessmann; P C M van Zijl; C Hock; P G Unschuld Journal: Sci Rep Date: 2016-10-17 Impact factor: 4.379
Authors: Julio Acosta-Cabronero; Guy B Williams; Arturo Cardenas-Blanco; Robert J Arnold; Victoria Lupson; Peter J Nestor Journal: PLoS One Date: 2013-11-21 Impact factor: 3.240